BoCo EDA - Andy Pickering
- Introduction
- Weather
- Bookings
- Load bookings data
- Let’s clean it up a little
- What percent of arrestees are transient?
- What percent of arrests are ‘antihomeless’ (true if any charges were related to ‘antihomeless charges’)
- What percent of arrests were made by Boulder PD?
- What agencies have the most arrests?
- Aggregate Monthly
- Aggregate by wkday
- By weekday, for separate years
- By weekday, for separate months
- Try aggregating to daily level?
- Relationship between arrests and weather
- Let’s merge the booking and weather data first
- Scatter plot daily arrests vs weather variables
- Scatter plot daily transient arrests vs weather variables
- ## Scatter plot daily antihomeless arrests vs weather variables
- Try a logistic regression of antihomeless arrest as function of temperature, precip, etc.?
- Look at individual years?
Introduction
- This project is looking at patterns of homelessness-related arrests and possible relationship to weather in Boulder County.
- It was started by Sam Zhang, who gathered and merged the data: https://github.com/samzhang111/boco-jail-analysis.
- Data is in a Google drive folder shared by Sam, which I have synced locally on my machine.
- This analysis is in a github repository: https://github.com/andypicke/boco_homeless
Hypotheses To Explore
- How does weather affect daily bookings? weather: downtown-boulder-weather.csv. bookings: bookings-with-transient-status.csv (you’ll need to aggregate this one up to the daily level)
- What proportion of jail bookings for housed/unhoused folks are for homelessness related charges? (by department? by year?) we pulled out charges that came from a list of antihomelessness ordinances compiled by some folks at Denver University, and marked them in the bookings dataset. If any of them were true, we marked the antihomeless column as true.
Weather
- Add more details about where weather data comes from (on Sam’s github?)
- What is SNWD? Some kind of accumulated snowfall?
- What are WT01,WT02 etc.?
- SNOW is missing data from about 2011-204?
suppressPackageStartupMessages(library(dplyr))
library(readr)
suppressPackageStartupMessages(library(lubridate))
library(ggplot2)Load weather data
wea <- read_csv('/Users/Andy/Google Drive/boco-jail/downtown-boulder-weather.csv',col_types = cols())
glimpse(wea)Observations: 6,400
Variables: 12
$ DATE <int> 20000101, 20000102, 20000103, 20000104, 20000105, 20000106, 200001...
$ PRCP <dbl> 0.00, 0.00, 0.08, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, ...
$ SNOW <dbl> 0.0, 0.0, 2.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0...
$ SNWD <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ TMAX <dbl> 54, 40, 36, 49, 47, 42, 47, 50, 45, 42, 61, 58, 41, 64, 63, 49, 64...
$ TMIN <dbl> 29, 22, 19, 13, 26, 16, 19, 23, 29, 30, 17, 41, 25, 25, 34, 22, 30...
$ WT01 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ WT03 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ WT04 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ WT05 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ WT06 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ WT11 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...summary(wea) DATE PRCP SNOW SNWD
Min. :20000101 Min. :0.00000 Min. : 0.0000 Length:6400
1st Qu.:20040519 1st Qu.:0.00000 1st Qu.: 0.0000 Class :character
Median :20081004 Median :0.00000 Median : 0.0000 Mode :character
Mean :20083356 Mean :0.05655 Mean : 0.2372
3rd Qu.:20130222 3rd Qu.:0.01000 3rd Qu.: 0.0000
Max. :20170714 Max. :9.08000 Max. :16.7000
NA's :5 NA's :1190
TMAX TMIN WT01 WT03 WT04
Min. : 6.00 Min. :-17.00 Min. :1 Min. :1 Length:6400
1st Qu.: 53.00 1st Qu.: 27.00 1st Qu.:1 1st Qu.:1 Class :character
Median : 67.00 Median : 38.00 Median :1 Median :1 Mode :character
Mean : 65.99 Mean : 38.17 Mean :1 Mean :1
3rd Qu.: 81.00 3rd Qu.: 51.00 3rd Qu.:1 3rd Qu.:1
Max. :102.00 Max. : 77.00 Max. :1 Max. :1
NA's :1 NA's :3 NA's :6304 NA's :5623
WT05 WT06 WT11
Min. :1 Min. :1 Min. :1
1st Qu.:1 1st Qu.:1 1st Qu.:1
Median :1 Median :1 Median :1
Mean :1 Mean :1 Mean :1
3rd Qu.:1 3rd Qu.:1 3rd Qu.:1
Max. :1 Max. :1 Max. :1
NA's :6337 NA's :6370 NA's :6331 names(wea) <- tolower(names(wea))
wea$date <- lubridate::ymd(wea$date)
wea <- wea %>% select( date,prcp,snow,tmax,tmin)
head(wea)Plot temperature timeseries
wea %>%
ggplot(aes(date,tmax))+
geom_point() +
ylab('Max Temp') +
ggtitle('Downtown Boulder Weather')
Plot precipiation timeseries
- I assume in inches?
wea %>%
ggplot(aes(date,prcp))+
geom_point() +
ylim(0,3)
Plot snow timeseries
- I assume this is daily snow fall in inches?
- Looks like we are missing a chunk of snowfall data from about 2011-2014 (pretty sure it snowed during those years :) )
wea %>%
ggplot(aes(date,snow))+
geom_point() +
ylim(0,3)
Bookings
Some info on the bookings-with-transient-status.csv data: - fta is “Failure to Appear” - ftc is “Failure to Comply” - (Sam) These are bookings in Boulder County Jail only. - (Sam) Each row is an individual booking. - (Sam) boulder means the arrest was made by Boulder PD - (Sam) Column transient for whether someone was homeless or not. - (Sam) I created indicator variables for a variety of antihomeless charges (that a local law school came up with). antihomeless is true if any of the antihomeless charges existed in the booking. So one source of error could be that if someone was arrested for an antihomeless charge AND a more serious offense, I still mark it as “antihomeless”. - (Sam) I don’t remember making any_antihomeless – I suspect it’s identical to antihomeless and introduced by accident - (Andy) There are two Booking Time columns? I will use booking_time for now, as it seems better. - (Andy) What are locations? Is that where they were booked? - (Andy) Booked and booking_time appear to be duplicates?
Load bookings data
bk <- read_csv('/Users/Andy/Google Drive/boco-jail/bookings-with-transient-status.csv',col_types = cols())
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|=====================================================================| 100% 33 MBnumber of columns of result is not a multiple of vector length (arg 1)46578 parsing failures.
row # A tibble: 5 x 5 col row col expected actual expected <int> <chr> <chr> <chr> actual 1 1537 Case No no trailing characters .0 file 2 1539 Case No no trailing characters .0 row 3 1540 Case No no trailing characters .0 col 4 1541 Case No no trailing characters .0 expected 5 1542 Case No no trailing characters .0 actual # ... with 1 more variables: file <chr>
... ................. ... ............................................. ........ ............................................. ...... ............................................. .... ............................................. ... ............................................. ... ............................................. ........ ............................................. ...... .......................................
See problems(...) for more details.glimpse(bk)Observations: 167,633
Variables: 30
$ Name <chr> "HOOD,AARON JAY", "LAWYER,KENNETH A", "AGUILAR TOR...
$ Booked <dttm> 2000-01-01 02:12:00, 2000-01-01 04:01:00, 2000-01...
$ Location <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ DOB <date> 1975-10-08, 1958-09-29, 1972-11-14, 1953-06-07, 1...
$ Race <chr> "W", "W", "W", "W", "W", "W", "W", "W", "W", "W", ...
$ Sex <chr> "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", ...
$ Case No <int> 991126052, 991001313, 991126053, 991031932, 991102...
$ Arresting Agency <chr> "UNIVERSITY OF COLORADO", "JAIL MITTS ONLY", "UNIV...
$ Arrest Date <date> 1999-12-31, 2000-01-16, 1999-12-31, 1999-12-31, 1...
$ camping <chr> "False", "False", "False", "False", "False", "Fals...
$ fta <chr> "True", "False", "False", "False", "False", "False...
$ ftc <chr> "False", "False", "False", "False", "False", "Fals...
$ booking_time <dttm> 2000-01-01 02:12:00, 2000-01-01 04:01:00, 2000-01...
$ boulder <chr> "False", "False", "False", "False", "False", "Fals...
$ urination <chr> "False", "False", "False", "False", "False", "Fals...
$ vehicle_as_residence <chr> "False", "False", "False", "False", "False", "Fals...
$ public_obstruct <chr> "False", "False", "False", "False", "False", "Fals...
$ public_trespass <chr> "False", "False", "False", "False", "False", "Fals...
$ begging <chr> "False", "False", "False", "False", "False", "Fals...
$ antihomeless <chr> "False", "False", "False", "False", "False", "Fals...
$ smoking <chr> "False", "False", "False", "False", "False", "Fals...
$ any_antihomeless <chr> "False", "False", "False", "False", "False", "Fals...
$ Address <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ City <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ State <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ ZIP Code <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ Booking Date <date> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
$ Booking Time <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ Facility <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ transient <chr> "False", "False", "False", "False", "False", "Fals...Let’s clean it up a little
# 'Booking Time' appears to be a duplicate/bad column
bk <- bk %>% select(-`Booking Time`)
# make columns names lowercase and remove spaces
names(bk) <- tolower(names(bk))
names(bk) <- gsub(' ','_',names(bk))
# some vars make more sense as factors
bk$arresting_agency <- as.factor(bk$arresting_agency)
bk$location <- as.factor(bk$location)
# add wkday,month,year so we can aggregate by those
bk$wkday <- lubridate::wday(bk$booking_date,label=TRUE)
bk$month_ <- lubridate::month(bk$arrest_date,label=TRUE)
bk$year <- lubridate::year(bk$arrest_date)
# modern reporting seems to start in 2000 (yearly totals go from less 100 before 2000, to order of 10,000 starting w/ 2000). Keep only 2000 on here.
bk <- bk %>% filter(year>=2000)
bk$booked_date <- lubridate::date(bk$booked)
# a bunch of vars should be logical
to_log <- function(a_col){
as.logical(a_col)
}
cols_to_log <- c('camping','boulder','urination','vehicle_as_residence','public_obstruct','public_trespass','begging','antihomeless','smoking','any_antihomeless','transient','fta','ftc')
bk[cols_to_log] <- lapply(bk[cols_to_log],to_log)
glimpse(bk)Observations: 163,939
Variables: 33
$ name <chr> "LAWYER,KENNETH A", "COPELAND,MARK WILLIAM", "DITZ...
$ booked <dttm> 2000-01-01 04:01:00, 2000-01-01 03:39:00, 2000-01...
$ location <fctr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
$ dob <date> 1958-09-29, 1953-01-27, 1948-01-25, 1977-03-15, 1...
$ race <chr> "W", "W", "W", "W", "W", "W", "W", "W", "W", "W", ...
$ sex <chr> "M", "M", "M", "F", "M", "M", "M", "F", "M", "M", ...
$ case_no <int> 991001313, 1089421, 1031978, 1103574, 5, 1101849, ...
$ arresting_agency <fctr> JAIL MITTS ONLY, LAFAYETTE PD, BOULDER PD, LONGMO...
$ arrest_date <date> 2000-01-16, 2000-01-01, 2000-01-01, 2000-01-01, 2...
$ camping <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ fta <lgl> FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FA...
$ ftc <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ booking_time <dttm> 2000-01-01 04:01:00, 2000-01-01 03:39:00, 2000-01...
$ boulder <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ urination <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ vehicle_as_residence <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ public_obstruct <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ public_trespass <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ begging <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ antihomeless <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ smoking <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ any_antihomeless <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ address <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ city <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ state <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ zip_code <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ booking_date <date> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
$ facility <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ transient <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ wkday <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ month_ <ord> Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, ...
$ year <dbl> 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 20...
$ booked_date <date> 2000-01-01, 2000-01-01, 2000-01-01, 2000-01-01, 2...What percent of arrestees are transient?
mean(bk$transient,na.rm = TRUE)*100[1] 13.05669What percent of arrests were made by Boulder PD?
mean(bk$boulder)*100[1] 6.589646levels(bk$arresting_agency) [1] "BOULDER COUNTY DRUG TASK FORCE" "BOULDER COUNTY SHERIFFS OFFICE"
[3] "BOULDER PD" "COLORADO STATE PATROL"
[5] "COMMUNITY CORRECTIONS" "DISTRICT ATTORNEYS OFFICE"
[7] "ERIE PD" "JAIL MITTS ONLY"
[9] "LAFAYETTE PD" "LONGMONT PD"
[11] "LOUISVILLE PD" "NEDERLAND MARSHALS OFFICE"
[13] "OTHER" "PAROLE"
[15] "STATE DIVISION OF WILDLIFE" "UNIVERSITY OF COLORADO"
[17] "WARD MARSHALS OFFICE" What agencies have the most arrests?
bk %>%
group_by(arresting_agency) %>%
tally() %>%
arrange( desc(n) ) %>%
ggplot(aes(x=reorder(arresting_agency,n),y=n))+
geom_bar(stat='identity',aes(fill=arresting_agency)) +
coord_flip()
Aggregate Monthly
- I thought there would be more arrests in winter, but there doesn’t seem to be that big of a difference.
bk %>%
filter(year %in% c(2011,2012,2013,2015,2015)) %>%
group_by(year,month_) %>%
tally() %>%
ggplot(aes(month_,n))+
geom_point() +
geom_bar(stat='identity',aes(fill=month_)) +
facet_wrap(~year)
Aggregate by wkday
bk %>%
filter(!is.na(wkday)) %>%
group_by(wkday) %>%
tally() %>%
ggplot( aes(wkday,n)) +
geom_col(aes(fill=wkday)) +
ggtitle('Total Arrests By Day, for ALL data')
By weekday, for separate years
bk %>%
filter(!is.na(wkday)) %>%
filter(year>1999) %>%
group_by(year,wkday) %>%
tally() %>%
ggplot( aes(wkday,n)) +
geom_col(aes(fill=wkday)) +
facet_wrap(~year) +
ggtitle('Total Arrests By Day, for each year')
By weekday, for separate months
bk %>%
filter(!is.na(wkday)) %>%
filter(year>1999) %>%
group_by(month_,wkday) %>%
tally() %>%
ggplot( aes(wkday,n)) +
geom_col(aes(fill=wkday)) +
facet_wrap(~month_) +
ggtitle('Total Arrests By Day, for each month, includes all years')
Try aggregating to daily level?
- Looks like daily number of arrests is decreasing over time?
- Not sure if actual, or something to do with how data is recorded.
bk %>%
filter(arrest_date>"2000-01-01") %>%
group_by(arrest_date) %>%
tally() %>%
ggplot(aes(arrest_date,n)) +
geom_point(alpha=0.2) +
ylim(0,60) +
geom_smooth(method="lm")
NARelationship between arrests and weather
Let’s merge the booking and weather data first
bk_wea <- left_join(bk,wea,by=c('arrest_date'='date'))
glimpse(bk_wea)Observations: 163,939
Variables: 37
$ name <chr> "LAWYER,KENNETH A", "COPELAND,MARK WILLIAM", "DITZ...
$ booked <dttm> 2000-01-01 04:01:00, 2000-01-01 03:39:00, 2000-01...
$ location <fctr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
$ dob <date> 1958-09-29, 1953-01-27, 1948-01-25, 1977-03-15, 1...
$ race <chr> "W", "W", "W", "W", "W", "W", "W", "W", "W", "W", ...
$ sex <chr> "M", "M", "M", "F", "M", "M", "M", "F", "M", "M", ...
$ case_no <int> 991001313, 1089421, 1031978, 1103574, 5, 1101849, ...
$ arresting_agency <fctr> JAIL MITTS ONLY, LAFAYETTE PD, BOULDER PD, LONGMO...
$ arrest_date <date> 2000-01-16, 2000-01-01, 2000-01-01, 2000-01-01, 2...
$ camping <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ fta <lgl> FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FA...
$ ftc <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ booking_time <dttm> 2000-01-01 04:01:00, 2000-01-01 03:39:00, 2000-01...
$ boulder <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ urination <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ vehicle_as_residence <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ public_obstruct <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ public_trespass <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ begging <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ antihomeless <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ smoking <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ any_antihomeless <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ address <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ city <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ state <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ zip_code <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ booking_date <date> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
$ facility <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ transient <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
$ wkday <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ month_ <ord> Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, ...
$ year <dbl> 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 20...
$ booked_date <date> 2000-01-01, 2000-01-01, 2000-01-01, 2000-01-01, 2...
$ prcp <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ snow <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ tmax <dbl> 49, 54, 54, 54, 54, 54, 54, 51, 54, 54, 54, 49, 54...
$ tmin <dbl> 22, 29, 29, 29, 29, 29, 29, 35, 29, 29, 29, 22, 29...Scatter plot daily arrests vs weather variables
- There doesn’t seem to be much of a relationship between daily # arrests and temperature.
bk %>%
group_by(arrest_date) %>%
tally() %>%
left_join(wea,by=c('arrest_date'='date')) %>%
ggplot(aes(tmin,n)) +
geom_point() +
geom_smooth(method = 'lm')
Scatter plot daily transient arrests vs weather variables
- Not too much for transient only either.
bk%>%
filter(transient==TRUE) %>%
group_by(arrest_date) %>%
tally() %>%
left_join(wea,by=c('arrest_date'='date')) %>%
ggplot(aes(tmin,n)) +
geom_point() +
geom_smooth(method = 'lm')
## Scatter plot daily antihomeless arrests vs weather variables
bk%>%
filter(antihomeless==TRUE) %>%
group_by(arrest_date) %>%
tally() %>%
left_join(wea,by=c('arrest_date'='date')) %>%
ggplot(aes(tmin,n)) +
geom_point() +
geom_smooth(method = 'lm')
Try a logistic regression of antihomeless arrest as function of temperature, precip, etc.?
Look at individual years?
bk_wea %>%
filter(year==2002) %>%
group_by(arrest_date) %>%
tally() %>%
left_join(wea,by=c('arrest_date'='date')) %>%
ggplot(aes(tmin,n)) +
geom_point() +
geom_smooth(method = 'lm')
bk %>%
group_by(arrest_date) %>%
tally() %>%
left_join(wea,by=c('arrest_date'='date')) %>%
ggplot(aes(arrest_date,tmin)) +
geom_point() +
geom_point(aes(x=arrest_date,y=n),color='red')
---
title: "BoCo EDA - Andy Pickering"
output: 
  html_document: 
    keep_md: yes
    toc: yes
  html_notebook: 
    toc: yes
---


# Introduction

- This project is looking at patterns of homelessness-related arrests and possible relationship to weather in Boulder County. 
- It was started by Sam Zhang, who gathered and merged the data: <https://github.com/samzhang111/boco-jail-analysis>.
- Data is in a Google drive folder shared by Sam, which I have synced locally on my machine.
- This analysis is in a github repository: <https://github.com/andypicke/boco_homeless>

## Hypotheses To Explore
- How does weather affect daily bookings? weather: *downtown-boulder-weather.csv*. bookings: *bookings-with-transient-status.csv* (you'll need to aggregate this one up to the daily level)
- What proportion of jail bookings for housed/unhoused folks are for homelessness related charges? (by department? by year?) we pulled out charges that came from a list of antihomelessness ordinances compiled by some folks at Denver University, and marked them in the bookings dataset. If any of them were true, we marked the *antihomeless* column as true.




# Weather

- Add more details about where weather data comes from (on Sam's github?)
- What is *SNWD*? Some kind of accumulated snowfall?
- What are *WT01*,*WT02* etc.?
- *SNOW* is missing data from about 2011-204?

```{r Load Libraries}
suppressPackageStartupMessages(library(dplyr))
library(readr)
suppressPackageStartupMessages(library(lubridate))
library(ggplot2)
```

## Load weather data
```{r}
wea <- read_csv('/Users/Andy/Google Drive/boco-jail/downtown-boulder-weather.csv',col_types = cols())
glimpse(wea)
```


```{r}
summary(wea)
```


```{r}
names(wea) <- tolower(names(wea))
wea$date <- lubridate::ymd(wea$date)
wea <- wea %>% select( date,prcp,snow,tmax,tmin)
head(wea)
```


## Plot temperature timeseries
```{r}
wea %>%
        ggplot(aes(date,tmax))+
        geom_point() +
        ylab('Max Temp') +
        ggtitle('Downtown Boulder Weather')
```

## Plot precipiation timeseries
- I assume in inches?
```{r}
wea %>%
        ggplot(aes(date,prcp))+
        geom_point() +
        ylim(0,3)
```


## Plot snow timeseries 
- I assume this is daily snow fall in inches?
- Looks like we are missing a chunk of snowfall data from about 2011-2014 (pretty sure it snowed during those years :) )
```{r}
wea %>%
        ggplot(aes(date,snow))+
        geom_point() +
        ylim(0,3)
```









# Bookings

Some info on the *bookings-with-transient-status.csv* data:
- *fta* is "Failure to Appear"
- *ftc* is "Failure to Comply"
- (Sam) These are bookings in Boulder County Jail only.
- (Sam) Each row is an individual booking.
- (Sam) `boulder` means the arrest was made by Boulder PD
- (Sam) Column `transient` for whether someone was homeless or not.
- (Sam) I created indicator variables for a variety of antihomeless charges (that a local law school came up with). `antihomeless` is true if any of the antihomeless charges existed in the booking. So one source of error could be that if someone was arrested for an antihomeless charge AND a more serious offense, I still mark it as "antihomeless". 
- (Sam) I don't remember making `any_antihomeless` -- I suspect it's identical to `antihomeless` and introduced by accident
- (Andy) There are two *Booking Time* columns? I will use *booking_time* for now, as it seems better.
- (Andy) What are *location*s? Is that where they were booked?
- (Andy) *Booked* and *booking_time* appear to be duplicates?

## Load bookings data
```{r}
bk <- read_csv('/Users/Andy/Google Drive/boco-jail/bookings-with-transient-status.csv',col_types = cols())
glimpse(bk)
```


## Let's clean it up a little
```{r}


# 'Booking Time' appears to be a duplicate/bad column
bk <- bk %>% select(-`Booking Time`)

# make columns names lowercase and remove spaces
names(bk) <- tolower(names(bk))
names(bk) <- gsub(' ','_',names(bk))

# some vars make more sense as factors
bk$arresting_agency <- as.factor(bk$arresting_agency)
bk$location <- as.factor(bk$location)

# add wkday,month,year so we can aggregate by those
bk$wkday <- lubridate::wday(bk$booking_date,label=TRUE)
bk$month_ <- lubridate::month(bk$arrest_date,label=TRUE)
bk$year <- lubridate::year(bk$arrest_date)

# modern reporting seems to start in 2000 (yearly totals go from less 100 before 2000, to order of 10,000 starting w/ 2000). Keep only 2000 on here.
bk <- bk %>% filter(year>=2000)


bk$booked_date <- lubridate::date(bk$booked)

# a bunch of vars should be logical
to_log <- function(a_col){
        as.logical(a_col)
}
cols_to_log <- c('camping','boulder','urination','vehicle_as_residence','public_obstruct','public_trespass','begging','antihomeless','smoking','any_antihomeless','transient','fta','ftc')
bk[cols_to_log] <- lapply(bk[cols_to_log],to_log)



glimpse(bk)
```




### What percent of arrestees are transient?
```{r}

mean(bk$transient,na.rm = TRUE)*100
```

### What percent of arrests are 'antihomeless' (true if any charges were related to 'antihomeless charges')
```{r}

mean(bk$antihomeless)*100
```

### What percent of arrests were made by Boulder PD?
```{r}
mean(bk$boulder)*100
```

###
```{r}
levels(bk$arresting_agency)
```


### What agencies have the most arrests?
```{r}

bk %>%
        group_by(arresting_agency) %>%
        tally() %>%
        arrange( desc(n) ) %>%
        ggplot(aes(x=reorder(arresting_agency,n),y=n))+
        geom_bar(stat='identity',aes(fill=arresting_agency)) +
        coord_flip()
```



### Aggregate Monthly
- I thought there would be more arrests in winter, but there doesn't seem to be that big of a difference.

```{r}
bk %>% 
        filter(year %in% c(2011,2012,2013,2015,2015)) %>%
        group_by(year,month_) %>%
        tally() %>%
        ggplot(aes(month_,n))+
        geom_point() +
        geom_bar(stat='identity',aes(fill=month_)) +
        facet_wrap(~year)
```


### Aggregate by wkday

```{r}
bk %>%
        filter(!is.na(wkday)) %>%
        group_by(wkday) %>%
        tally() %>%
        ggplot( aes(wkday,n)) +
        geom_col(aes(fill=wkday)) +
        ggtitle('Total Arrests By Day, for ALL data')
```

### By weekday, for separate years
```{r}
bk %>%
        filter(!is.na(wkday)) %>%
        filter(year>1999) %>%
        group_by(year,wkday) %>%
        tally() %>%
        ggplot( aes(wkday,n)) +
        geom_col(aes(fill=wkday)) +
        facet_wrap(~year) +
        ggtitle('Total Arrests By Day, for each year')
```


### By weekday, for separate months
```{r}
bk %>%
        filter(!is.na(wkday)) %>%
        filter(year>1999) %>%
        group_by(month_,wkday) %>%
        tally() %>%
        ggplot( aes(wkday,n)) +
        geom_col(aes(fill=wkday)) +
        facet_wrap(~month_) +
        ggtitle('Total Arrests By Day, for each month, includes all years')
```

### Try aggregating to daily level?
- Looks like daily number of arrests is decreasing over time? 
- Not sure if actual, or something to do with how data is recorded.
```{r}
bk %>% 
        filter(arrest_date>"2000-01-01") %>%
        group_by(arrest_date) %>%
        tally() %>%
        ggplot(aes(arrest_date,n)) +
        geom_point(alpha=0.2) +
        ylim(0,60) +
        geom_smooth(method="lm")
        
```


# Relationship between arrests and weather

## Let's merge the booking and weather data first

```{r}
bk_wea <- left_join(bk,wea,by=c('arrest_date'='date'))
glimpse(bk_wea)
```

## Scatter plot daily arrests vs weather variables
- There doesn't seem to be much of a relationship between daily # arrests and temperature. 
```{r}
bk %>%
        group_by(arrest_date) %>% 
        tally() %>% 
        left_join(wea,by=c('arrest_date'='date')) %>%
        ggplot(aes(tmin,n)) +
        geom_point() +
        geom_smooth(method = 'lm')
```



## Scatter plot daily _transient_ arrests vs weather variables
- Not too much for transient only either.
```{r}
bk%>%
        filter(transient==TRUE) %>% 
        group_by(arrest_date) %>% 
        tally() %>% 
        left_join(wea,by=c('arrest_date'='date')) %>%
        ggplot(aes(tmin,n)) +
        geom_point() +
        geom_smooth(method = 'lm')
```


## Scatter plot daily _antihomeless_ arrests vs weather variables
- 

```{r}
bk%>%
        filter(antihomeless==TRUE) %>% 
        group_by(arrest_date) %>% 
        tally() %>% 
        left_join(wea,by=c('arrest_date'='date')) %>%
        ggplot(aes(tmin,n)) +
        geom_point() +
        geom_smooth(method = 'lm')
```

## Try a logistic regression of antihomeless arrest as function of temperature, precip, etc.?


## Look at individual years?
```{r}
bk_wea %>%
        filter(year==2002) %>% 
        group_by(arrest_date) %>% 
        tally() %>% 
        left_join(wea,by=c('arrest_date'='date')) %>%
        ggplot(aes(tmin,n)) +
        geom_point() +
        geom_smooth(method = 'lm')
```


```{r}
bk %>% 
        group_by(arrest_date) %>% 
        tally() %>% 
        left_join(wea,by=c('arrest_date'='date')) %>% 
        ggplot(aes(arrest_date,tmin)) +
        geom_point() +
        geom_point(aes(x=arrest_date,y=n),color='red')
```


